10 Best Machine Learning Courses Worth Your Time in 2024

Machine learning ain’t just for the techies anymore. You’ve heard of it, but that doesn’t mean you have any idea what that refers to or why it’s even relevant.

AI is alive and well, and it’s changing our world faster than ever. So catch the wave before you get left behind! 

Industries are shifting their techniques at a breakneck pace—there are healthcare physicians predicting diseases and banks nabbing fraudsters in their tracks, and machine learning is at the center of it all. 

We’ve done our due diligence and searched the web far and wide, digging through reviews, and talking to experts to give you the lowdown on 10 courses that really deliver in 2024. It’s time to level up!

The 10 Best Machine Learning Courses in 2024

What makes a machine learning course worth your hard-earned cash and precious time? That’s a fair question, and we’re glad you asked. 

There are quality courses that are comprehensive, hands-on, taught by folks who actually know their stuff, and, most importantly, get you results. There’s no better way to learn than to learn from people who have been there and done it.

1. Machine Learning Specialization (Coursera)

Coursera has earned its place as a top-notch online learning tool. This specialization, created by deeplearning.ai and Stanford University, is everything a beginner could ever want. It’s does exactly you need—nothing more and nothing less by covering all the basics of machine learning:

• How it actually works: You are going to learn about different algorithms and how they learn from data.
• The tools of the trade: Get comfy with popular libraries like TensorFlow and scikit-learn.
• Hands-on practice: Get into real-world projects to apply what you’ve learned.

Pros:

• World-class instructors with the credentials to back it up.
• A well-structured curriculum that builds your knowledge step-by-step.
• Affordable (financial aid is available).

Cons:

• Can feel a bit slow for those who already have some programming experience.
• Not as deep a dive into advanced topics as some other courses.

At a Glance:

• Duration: Approximately 5 months (at 5 hours/week)
• Cost: Varies, financial aid available
• Prerequisites: Basic programming knowledge recommended

The Verdict: This one is just what the doctor ordered if you’re new to machine learning. It’s a solid foundation and sets you up for success in more advanced courses.

2. AWS Machine Learning Engineer Nanodegree (Udacity)

Cloud computing and machine learning? A match made in heaven. This nanodegree program from Udacity will help you master machine learning in the AWS cloud environment.

• Cloud-centric: You will learn to build, deploy, and scale machine learning models on Amazon’s powerful platform.
• Real-world projects: Get set to work on projects like building a recommendation system or a fraud detector.
• Career services: Get help with your resume, LinkedIn profile, and job search strategy.

Pros:

• Strong focus on practical skills that employers actually want.
• Access to industry experts and mentors.
• Excellent career support.

Cons:

• On the pricier side compared to some other options.
• Requires some prior knowledge of machine learning and Python.

At a Glance:

• Duration: 4 months (at 10 hours/week)
• Cost: $399/month
• Prerequisites: Intermediate Python and machine learning fundamentals

The Verdict: Getting a machine learning engineering job that uses AWS is within your reach with nanodegree. It’s really a no brainer! It gives you with the skills and the confidence to tackle real-world challenges in the cloud.

3. Machine Learning A-Z™: Hands-On Python & R In Data Science (Udemy)

Udemy, the land of never-ending sales, offers this jam-packed course that covers both Python and R for machine learning. It’s a deep dive into the nitty-gritty:

• Algorithm galore: Learn about regression, classification, clustering, and more.
• Data preprocessing: Get your data squeaky clean and ready for analysis.
• Model evaluation: Figure out how to pick the best model for your specific problem.

Pros:

• Huge amount of content for a relatively low price.
• Covers both Python and R, giving you flexibility.
• Lots of hands-on exercises to solidify your learning.

Cons:

• Can feel a bit overwhelming for beginners due to the sheer volume of information.
• Some topics could be explained more clearly.

At a Glance:

• Duration: 44.5 hours of on-demand video
• Cost: Varies, often on sale for under $20
• Prerequisites: Basic knowledge of Python or R is helpful

The Verdict: This course is a solid choice for those who are already familiar with Python or R and want to deepen their understanding of machine learning algorithms. It’s not the best for complete beginners, but it’s a great resource for intermediate learners.

4. Deep Learning Specialization (Coursera)

Ready to dive into the deep end? This specialization, also from deeplearning.ai, takes you beyond the basics and into the world of neural networks and deep learning. You’ll build models that can recognize images, understand language, and even generate creative content.

• Neural networks unleashed: Learn about different types of neural networks and how to train them.
• Cutting-edge techniques: Explore advanced concepts like convolutional networks, recurrent networks, and transformers.
• Real-world applications: Tackle projects like building an image classifier or a language translation model.

Pros:

• Taught by Andrew Ng, one of the biggest names in deep learning.
• Comprehensive coverage of deep learning fundamentals and advanced topics.
• Challenging but rewarding projects.

Cons:

• Requires a solid understanding of machine learning basics.
• Can be mathematically intense for some learners.

At a Glance:

• Duration: Approximately 3 months (at 5 hours/week)
• Cost: Varies, financial aid available
• Prerequisites: Machine Learning Specialization or equivalent knowledge

The Verdict: This specialization is a must-do for anyone serious about a career in deep learning. It’s tough, but it will equip you with the skills you need to build cutting-edge AI models.

5. TensorFlow Developer Professional Certificate (Coursera)

Ready to become a TensorFlow ninja? This professional certificate program will turn you into a TensorFlow expert. You’ll learn how to build, train, and deploy sophisticated deep learning models using this powerful framework.

• TensorFlow mastery: Become proficient in using TensorFlow’s core APIs and tools.
• Hands-on experience: Build and deploy real-world deep learning models for image classification, natural language processing, and time series forecasting.
• Industry recognition: Earn a professional certificate that demonstrates your TensorFlow expertise to potential employers.

Pros:

• Comprehensive coverage of TensorFlow’s features and capabilities.
• Strong emphasis on practical skills and real-world applications.
• Valuable credential for job seekers.

Cons:

• Assumes some prior experience with machine learning and Python.
• Can be challenging for beginners.

At a Glance:

• Duration: Approximately 4 months (at 5 hours/week)
• Cost: Varies, financial aid available
• Prerequisites: Some experience with machine learning and Python recommended

The Verdict: If you want to become a sought-after TensorFlow developer, this certificate program is the way to go. It’s a challenging but rewarding journey that will open doors to exciting career opportunities.

6. Reinforcement Learning Specialization (Coursera)

Ever wondered how AI agents learn to play games like Go or navigate complex environments? Reinforcement learning is the secret sauce. This specialization will teach you how to build intelligent agents that can learn from their own actions and achieve impressive results.

• Reinforcement learning foundations: Understand the key concepts and algorithms behind reinforcement learning.
• Hands-on practice: Build your own reinforcement learning agents for various tasks.
• Real-world applications: Explore how reinforcement learning is used in robotics, game playing, and resource management.

Pros:

• Cutting-edge topic with immense potential in various fields.
• Taught by leading experts in reinforcement learning.
• Challenging but fascinating projects.

Cons:

• Requires a strong understanding of machine learning and programming.
• Not for the faint of heart – reinforcement learning can be complex.

At a Glance:

• Duration: Approximately 4 months (at 5 hours/week)
• Cost: Varies, financial aid available
• Prerequisites: Machine Learning Specialization or equivalent knowledge

The Verdict: This specialization is ideal for those who want to explore the exciting frontier of reinforcement learning. It’s a challenging but rewarding path that will give you a competitive edge in the job market.

7. Machine Learning with Python (DataCamp)

For those who like to learn by doing, DataCamp’s interactive courses are a fantastic option. Their “Machine Learning with Python” track is a great way to get hands-on with popular libraries like scikit-learn and pandas.

• Interactive learning: Work through bite-sized exercises and challenges to solidify your understanding.
• Focus on Python: Master the essential Python tools for machine learning.
• Career track: Progress through a structured sequence of courses to build your skills systematically.

Pros:

• Fun and engaging way to learn.
• Immediate feedback and support.
• Flexible, self-paced format.

Cons:

• Subscription-based pricing (but a free trial is available).
• Less in-depth than some other courses.

At a Glance:

• Duration: Varies depending on your pace
• Cost: Subscription-based (free trial available)
• Prerequisites: Basic Python knowledge

The Verdict: This track is perfect for beginners who want to learn the practical side of machine learning with Python. It’s a fun and effective way to build your skills and confidence.

8. Fast.ai Practical Deep Learning for Coders

Fast.ai takes a unique, top-down approach to teaching deep learning. You’ll dive straight into building state-of-the-art models using cutting-edge libraries like PyTorch, and then gradually peel back the layers to understand the underlying theory.

• Cutting-edge tools: Get up to speed with the latest deep learning libraries and techniques.
• Practical focus: Learn by building real-world projects like image classifiers and language models.
• Active community: Join a vibrant community of learners and practitioners.

Pros:

• Free and open-source.
• Hands-on, project-based learning.
• Great for those who learn best by doing.

Cons:

• Can be challenging for beginners with no programming experience.
• Less structured than some other courses.

At a Glance:

• Duration: Approximately 7 weeks (self-paced)
• Cost: Free
• Prerequisites: Some Python programming experience is recommended

The Verdict: This course is a gem for those who want to get their hands dirty with deep learning fast. It’s not the easiest, but it’s a fantastic way to learn the practical skills that are in high demand.

9. MicroMasters® Program in Statistics and Data Science (MITx on edX)

Looking for a more rigorous academic program? MIT’s MicroMasters program is a graduate-level series of courses that covers the fundamentals of statistics and data science, including machine learning.

• Rigorous curriculum: Learn from MIT faculty and explore advanced topics in depth.
• Academic credential: Earn a MicroMasters credential that can be applied towards a full Master’s degree at MIT.
• Career advancement: Gain the knowledge and skills to excel in data science roles.

Pros:

• World-class education from a prestigious institution.
• Comprehensive coverage of statistics and data science.
• Valuable credential for your resume.

Cons:

• Challenging workload and rigorous exams.
• More expensive than many other online courses.

At a Glance:

• Duration: Approximately 1 year (self-paced)
• Cost: $1,350
• Prerequisites: Strong math background (calculus, linear algebra, probability)

The Verdict: This program is ideal for those who want a rigorous academic foundation in statistics and data science. It’s a challenging but rewarding path that can open doors to top-tier careers.

10. Machine Learning Engineer Bootcamp (Springboard)

Want a career-focused program with personalized mentorship? Springboard’s bootcamp will prepare you for a machine learning engineering role through a combination of online coursework, 1:1 mentorship, and career coaching.

• Mentorship-driven: Receive guidance and support from experienced machine learning engineers.
• Career-focused: Learn the skills you need to land a job in the field.
• Job guarantee: Springboard offers a job guarantee – if you don’t get a job within six months of graduation, you get your money back.

Pros:

• Personalized mentorship and career support.
• Strong focus on practical skills and job readiness.
• Job guarantee provides peace of mind.

Cons:

• Significant time and financial investment.
• Requires a competitive application process.

At a Glance:

• Duration: 9 months (part-time, flexible schedule)
• Cost: $11,900
• Prerequisites: Some programming experience and a strong math background

The Verdict: If you’re serious about making a career switch into machine learning engineering, this bootcamp is a great option. It’s a challenging but rewarding journey that will equip you with the skills and confidence to succeed in the field.

How To Choose the Right Machine Learning Course for You

With so many options out there, how do you pick the course that’s right for you? Here are a few things to consider:

• Your skill level: Are you a newbie or a seasoned pro? Choose a course that matches your current knowledge.
• Your goals: What do you want to achieve with machine learning? Do you want to build a career, solve a specific problem, or just learn for fun?
• Your learning style: Do you prefer structured courses or more flexible, self-paced learning?
• Your budget: Some courses are free, while others can cost thousands of dollars.

Final Thoughts 

Take 2024 by storm when you put yourself out there and start learning the skills that are really going to impact the job market in the years to come. 

Machine learning courses aren’t limited to those aspiring to have a career in AI. It’s for the person who wants to tackle a specific project, or the individual who simply has a burning curiosity for this mind-blowing tech.

Choosing the right course comes down to what you like and what you need. They’re not necessarily cheap, so we know you may not be able to do the most experimentation. BUT, we advise you to try at least two different styles so you can figure out what really works for you. 

The most important thing is to take that first step and start your machine learning adventure today. What are you waiting for? You ain’t gettin’ any younger, and upskilling is a thing, folks!